The resulting algorithm, Neural Neighbor Style Transfer (NNST), is a straightforward new baseline for style transfer based on replacing content features with their nearest neighbor style feature (in the feature space of a neural network). It offers state-of-the-art visual quality and comes in two variants. The fast variant NNST-D (NNST-Decoder) produces a 512x512 output in \(\approx 4.5\)  seconds by using a learned decoder to efficiently recover pixels from a rearranged tensor of style features. The slower variant NNST-Opt (NNST-Optimization) trades off speed for quality, producing a 512x512 output in \(\approx 40\)  seconds, by directly optimizes the pixels of the output image in the same manner as Gatys [1]}, STROTSS [2]}, and others [3]}, [4]}.
